ISDS Conference

In France, surveillance of seasonal gastroenteritis uses to be monitored by an information system based on a computer network of physicians so called Sentinel Network (1). Regionally, the use of this system as limitations. SOS Medecin is an organization of general practitioners, present in many French cities, which undertakes home medical visits 24hrs a day, 7 days a week. In Bordeaux, this organization makes a daily transmission of every diagnostic related to their visits.

Objective

To construct an indicator adapted for syndromic surveillance of seasonal gastroenteritis based on data from "SOS Medecin" in the city of Bordeaux, France.

In 2004, the BioDefend (BD) syndromic surveillance (SS) system was implemented in Duval County hospitals (Jacksonville, FL). Daily emergency department chief complaints are manually classified and entered into the BD system by triage personnel. As part of a statewide implementation, the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) began collecting data in the Jacksonville area during the winter of 2007-08. ESSENCE uses an automated data collection, chief complaint parsing and analysis process for data management and analysis. The use of two systems during the same period of time in one area provided a unique opportunity to retrospectively analyze characteristics of the BD and ESSENCE systems.

Objective

To compare detection of a community outbreak of influenza-like illness using two SS systems, one using a clinician’s classification of reason for visit and the other using an automated chief complaint parsing algorithm.

The New York City Department of Health and Mental Hygiene (NYC DOHMH) collects data daily from 50 of 61 (82%) emergency departments (EDs) in NYC representing 94% of all ED visits (avg daily visits ~10,000). The information collected includes the date and time of visit, age, sex, home zip code and chief complaint of each patient. Observations are assigned to syndromes based on the chief complaint field and are analyzed using SaTScan to identify statistically significant clusters of syndromes at the zip code and hospital level. SaTScan employs a circular spatial scan statistic and clusters that are not circular in nature may be more difficult to detect. FlexScan employs a flexible scan statistic using an adjacency matrix design.

Objective

To use the NYC DOHMH's ED syndromic surveillance data to evaluate FleXScan’s flexible scan statistic and compare it to results from the SaTScan circular scan. A second objective is to improve cluster detection in by improving geographic characteristics of the input files.

Emerging infections, both natural and intentional, have provided an impetus for improved disease surveillance and response. The recognition of the interdependence of health care systems and public health infrastructure provides an opportunity to expand beyond traditional disease-based surveillance to a more comprehensive, integrated approach that leverages existing electronic information. The Veterans Affairs (VA) hospital system is uniquely positioned to perform multi-institutional enhanced electronic surveillance. A wealth of electronic information and technology resources are available in all VA hospitals and their associated clinics, as each facility uses the same standardized Computer Patient Record System. Influenza-like illness (ILI) is a common clinical syndrome of diverse etiology that presents with respiratory and systemic symptoms. The NC health department mandates the reporting of ILI from emergency departments to facilitate monitoring of seasonal ILI and serve as an important component of pandemic preparedness. Existing surveillance systems utilize an ICD-9 respiratory code screen and subsequent manual chart review which is timeconsuming and insensitive. Automated medical record review using more comprehensive electronic data may improve the system’s timeliness and efficiency.

Objective

To use data collected by NC-VET to create an automated ILI surveillance program and compare its accuracy and efficiency to the existing program.

It is well known that diabetic patients are particularly sensitive to infections however no robust diagnostic test for the early detection of infection has been developed to date. Glucose levels  would be an ideal indicator, since diabetics measure their blood glucose (BG) on a daily basis along with insulin intake. At the same time some computerized systems have been developed that collect BG values using sensors and transmit them to a central data repository, such as the Electronic Healthcare Record. Acute infection often results in hyperglycemia, due to release of regulatory hormones and pro-inflammatory cytokines as evidenced by studies on hospitalized patients. Nevertheless the underlying mechanisms of infection-related stress hyperglycemia are not fully understood.

Objective

The aim of the study is to assess the correlation between blood glucose levels and infection and to propose the development of a model for the early detection of infections in diabetics.

Many syndromic surveillance systems have been developed and are operational, yet lack concise guidelines for investigating and conducting followups on daily alarms. Daily emergency department visits from six reporting hospitals in the Duval County area are assessed and classified into a BioDefend (BD) system entry by triage personnel. Alarms are categorized into alerts, 3 SD above a 30 day rolling mean, or warnings, 2-3 SD above the mean. Signals are monitored and in response, public health investigations and recommended interventions are initiated.

Objective

To evaluate the protocol that the Duval County Health Department (DCHD) epidemiology staff uses to respond to BD syndromic surveillance system alarms. The response protocol utilizes all signals detected by BD and its secondary resources, within the DCHD jurisdiction.

Under a grant from the Centers for Disease Control and Prevention (CDC), the DC DOH established the Environmental Public Health Tracking Program (EPHTP) to monitor specific environmental and public health indicators and to investigate any potential links for the purpose of guiding policy development, resource allocation, and decision-making on disease prevention and treatment activities. This information improves understanding of the immediate and short-term effects of airborne pollutants on health care usage. In a collaborative project between JHU/APL and DC DOH, investigators explored and quantified correlations between ambient air quality measurements from five DC stations between October 2001 and March 2004 and DC hospital pediatric emergency department (ED) visits for asthma exacerbations. 

Objective

The study objective was to provide the CDC results from the EPHTP on quantifying the relationship between air quality and pediatric ED visits for asthma among DC residents over a 3 year period. This effort also explored novel uses of traditional data to understand background disease patterns so that unexpected fluctuations could be better detected in community disease trends and thereby identify early disease outbreaks.

One of the first county-wide syndromic surveillance systems in the nation, the Syndromic Tracking and Reporting System (STARS) has been in operation since 11/01/2001, and now covers Hillsborough, Pinellas and Collier counties. STARS uses hospital emergency department visit data to detect aberrations of non-specific syndromes and serves as an earlier warning system for public health threats. Patient’s syndrome is collected upon arrival, separately from routine collection of clinical and administrative data; but in some hospitals the process is being streamlined with routine data collection. Aberration detection is done twice daily using the statistical system EARS developed by the CDC. Upon flagging of an aberration, follow-up investigation is conducted to verify cases, and identify source of exposure following a sequence of decision procedure. After several years of operation and some instituted enhancements, a systematic evaluation was called to (1) assess if STARS has met the operation specifications and (2) characterize system efficacy and effectiveness.

Objective

To evaluate STARS with respect to quality of syndrome diagnoses, timeliness and completeness of data collection and processing, performance of aberration detection methods, and aberration investigation.

Clinical and public health microbiology laboratories of the world are a rich, underutilized resource in monitoring the changing epidemiology of microbial populations worldwide. Two areas of public health importance in which effective use of relevant local data are critical include: 1. guiding local treatment guidelines, informed by knowledge of local patterns of infection and antimicrobial resistance; and 2. the early identification and characterization of outbreaks.

Most laboratories in the developed world and many in the developing world have clinical databases designed to meet the day-to-day needs of clinical reporting, specimen processing, billing, and permanent information storage. Unfortunately, most such systems were not developed with the epidemiological needs of microbiologists, infection control staff, public health authorities, and policy-makers in mind. To address this critical gap, our group at the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance has developed the WHONET and BacLink softwares to support local, national, and international infectious disease surveillance programs.

Objective

This paper describes two free softwares developed for the automated and semi-automated capture, processing, and analysis of microbiology laboratory data. Applications include early detection of hospital and community outbreaks, guiding local treatment guidelines and public health policy, and immediate alert of important pathogens and potential errors in laboratory testing.

One criterion for evaluating the effectiveness of a surveillance system is the system’s positive predictive value. To our knowledge few studies have described the positive predictive value of syndromic surveillance signals for naturally occurring conditions of public health importance.

Objective

We evaluated the positive predictive value of signals detected by our syndromic surveillance system.